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Journal of Nuclear Medicine Technology

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Research ArticleIMAGING

Comparison of a Noise-Weighted Filtered Backprojection Algorithm with the Standard MLEM Algorithm for Poisson Noise

Gengsheng L. Zeng
Journal of Nuclear Medicine Technology December 2013, 41 (4) 283-288; DOI: https://doi.org/10.2967/jnmt.113.127399
Gengsheng L. Zeng
Department of Engineering, Weber State University, Ogden, Utah, and Utah Center for Advanced Imaging Research (UCAIR), Department of Radiology, University of Utah, Salt Lake City, Utah
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Figures

  • FIGURE 1.
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    FIGURE 1.

    Window functions (gain vs. frequency) for k = 6,000 and n = 1, 2,…, 10.

  • FIGURE 2.
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    FIGURE 2.

    Window functions (gain vs. frequency) for k = 1,868 and n = 1, 2,…, 10.

  • FIGURE 3.
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    FIGURE 3.

    Window functions (gain vs. frequency) for n = 5 and 5 different k values.

  • FIGURE 4.
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    FIGURE 4.

    Results of computer simulations using phantom 1.

  • FIGURE 5.
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    FIGURE 5.

    Results of computer simulations using phantom 2.

  • FIGURE 6.
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    FIGURE 6.

    Bias vs. SD for computer simulations with phantom 1. From right to left, each dot represents noise level case in Figure 4 from top to bottom. Leftmost dot is for noiseless case.

  • FIGURE 7.
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    FIGURE 7.

    Bias vs. SD for computer simulations with phantom 2. From right to left, each dot represents noise level case in Figure 5 from top to bottom. Leftmost dot is for noiseless case.

  • FIGURE 8.
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    FIGURE 8.

    Results of real SPECT torso/heart phantom.

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Journal of Nuclear Medicine Technology: 41 (4)
Journal of Nuclear Medicine Technology
Vol. 41, Issue 4
December 1, 2013
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Comparison of a Noise-Weighted Filtered Backprojection Algorithm with the Standard MLEM Algorithm for Poisson Noise
Gengsheng L. Zeng
Journal of Nuclear Medicine Technology Dec 2013, 41 (4) 283-288; DOI: 10.2967/jnmt.113.127399

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Comparison of a Noise-Weighted Filtered Backprojection Algorithm with the Standard MLEM Algorithm for Poisson Noise
Gengsheng L. Zeng
Journal of Nuclear Medicine Technology Dec 2013, 41 (4) 283-288; DOI: 10.2967/jnmt.113.127399
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Keywords

  • image reconstruction
  • ML-EM algorithm
  • filtered backprojection algorithm
  • Poisson noise
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